Overview

Dataset statistics

Number of variables36
Number of observations755
Missing cells39
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory212.5 KiB
Average record size in memory288.2 B

Variable types

CAT18
NUM16
BOOL2

Warnings

TempDist has constant value "755" Constant
SpatDist has constant value "755" Constant
TempGL has constant value "755" Constant
SpatGL has constant value "755" Constant
Bes2 has constant value "755" Constant
SpatIL is highly correlated with TempILHigh correlation
TempIL is highly correlated with SpatILHigh correlation
Lich2 is highly correlated with Lich1High correlation
Lich1 is highly correlated with Lich2High correlation
Fstf has 39 (5.2%) missing values Missing
df_index has unique values Unique
UArt1 has 29 (3.8%) zeros Zeros
AUrs1 has 715 (94.7%) zeros Zeros

Reproduction

Analysis started2020-10-30 17:17:42.260527
Analysis finished2020-10-30 17:18:51.523189
Duration1 minute and 9.26 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct755
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean946.6476821
Minimum0
Maximum1864
Zeros1
Zeros (%)0.1%
Memory size5.9 KiB
2020-10-30T18:18:51.939086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile96.4
Q1507.5
median964
Q31388.5
95-th percentile1752.6
Maximum1864
Range1864
Interquartile range (IQR)881

Descriptive statistics

Standard deviation526.9047527
Coefficient of variation (CV)0.5566006896
Kurtosis-1.129042672
Mean946.6476821
Median Absolute Deviation (MAD)437
Skewness-0.07347041414
Sum714719
Variance277628.6184
MonotocityStrictly increasing
2020-10-30T18:18:52.094212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
102310.1%
 
134410.1%
 
33110.1%
 
33010.1%
 
135310.1%
 
144210.1%
 
168410.1%
 
32510.1%
 
32410.1%
 
32310.1%
 
Other values (745)74598.7%
 
ValueCountFrequency (%) 
010.1%
 
210.1%
 
410.1%
 
510.1%
 
610.1%
 
ValueCountFrequency (%) 
186410.1%
 
185910.1%
 
185810.1%
 
185410.1%
 
185210.1%
 

TempMax
Real number (ℝ≥0)

Distinct200
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.8913907
Minimum9
Maximum1341
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-10-30T18:18:52.258312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile41.1
Q1106.5
median189
Q3325.5
95-th percentile664.8
Maximum1341
Range1332
Interquartile range (IQR)219

Descriptive statistics

Standard deviation203.9662331
Coefficient of variation (CV)0.8228048281
Kurtosis4.814692333
Mean247.8913907
Median Absolute Deviation (MAD)99
Skewness1.864160618
Sum187158
Variance41602.22426
MonotocityNot monotonic
2020-10-30T18:18:52.434641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
78152.0%
 
129131.7%
 
135111.5%
 
162111.5%
 
264111.5%
 
99101.3%
 
87101.3%
 
138101.3%
 
8191.2%
 
11791.2%
 
Other values (190)64685.6%
 
ValueCountFrequency (%) 
910.1%
 
1510.1%
 
1850.7%
 
2140.5%
 
2410.1%
 
ValueCountFrequency (%) 
134110.1%
 
132320.3%
 
125710.1%
 
119410.1%
 
102910.1%
 

TempAvg
Real number (ℝ≥0)

Distinct211
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.85695364
Minimum3
Maximum1326
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-10-30T18:18:52.604713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile15
Q140.5
median70
Q3106
95-th percentile235
Maximum1326
Range1323
Interquartile range (IQR)65.5

Descriptive statistics

Standard deviation91.81710103
Coefficient of variation (CV)1.021814087
Kurtosis56.01190542
Mean89.85695364
Median Absolute Deviation (MAD)32
Skewness5.530837802
Sum67842
Variance8430.380041
MonotocityNot monotonic
2020-10-30T18:18:52.775395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
44172.3%
 
53141.9%
 
89131.7%
 
26121.6%
 
52101.3%
 
23101.3%
 
61101.3%
 
76101.3%
 
81101.3%
 
5691.2%
 
Other values (201)64084.8%
 
ValueCountFrequency (%) 
310.1%
 
410.1%
 
520.3%
 
630.4%
 
730.4%
 
ValueCountFrequency (%) 
132610.1%
 
92010.1%
 
70310.1%
 
57510.1%
 
53310.1%
 

SpatMax
Real number (ℝ≥0)

Distinct643
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17118.90331
Minimum1036
Maximum219082
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-10-30T18:18:53.091554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1036
5-th percentile2851.9
Q16788.5
median12122
Q320173
95-th percentile38702.5
Maximum219082
Range218046
Interquartile range (IQR)13384.5

Descriptive statistics

Standard deviation23665.4182
Coefficient of variation (CV)1.382414386
Kurtosis44.42453065
Mean17118.90331
Median Absolute Deviation (MAD)6069
Skewness6.137766206
Sum12924772
Variance560052018.8
MonotocityNot monotonic
2020-10-30T18:18:53.236034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1316381.1%
 
235140.5%
 
18973040.5%
 
602540.5%
 
662140.5%
 
906530.4%
 
2046930.4%
 
1974630.4%
 
1345630.4%
 
21908230.4%
 
Other values (633)71694.8%
 
ValueCountFrequency (%) 
103610.1%
 
117610.1%
 
120620.3%
 
131510.1%
 
141510.1%
 
ValueCountFrequency (%) 
21908230.4%
 
19531020.3%
 
18973040.5%
 
15323710.1%
 
13578010.1%
 

SpatAvg
Real number (ℝ≥0)

Distinct648
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4374.668874
Minimum358
Maximum17805
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-10-30T18:18:53.386268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum358
5-th percentile1156.1
Q12364
median3835
Q36009.5
95-th percentile9520.6
Maximum17805
Range17447
Interquartile range (IQR)3645.5

Descriptive statistics

Standard deviation2642.116014
Coefficient of variation (CV)0.6039579428
Kurtosis1.649895619
Mean4374.668874
Median Absolute Deviation (MAD)1763
Skewness1.114719761
Sum3302875
Variance6980777.031
MonotocityNot monotonic
2020-10-30T18:18:53.525985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
560650.7%
 
1026640.5%
 
296130.4%
 
406630.4%
 
352030.4%
 
437930.4%
 
657530.4%
 
793330.4%
 
442330.4%
 
889030.4%
 
Other values (638)72295.6%
 
ValueCountFrequency (%) 
35810.1%
 
39310.1%
 
54410.1%
 
64310.1%
 
66010.1%
 
ValueCountFrequency (%) 
1780510.1%
 
1513210.1%
 
1478510.1%
 
1419810.1%
 
1374410.1%
 

TempDist
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
755 
ValueCountFrequency (%) 
0755100.0%
 
2020-10-30T18:18:53.630013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

SpatDist
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
755 
ValueCountFrequency (%) 
0755100.0%
 
2020-10-30T18:18:53.675982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Coverage
Real number (ℝ≥0)

Distinct82
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.2410596
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-10-30T18:18:53.763550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q121
median31
Q344
95-th percentile69
Maximum100
Range98
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.90907358
Coefficient of variation (CV)0.5230291875
Kurtosis0.5238877505
Mean34.2410596
Median Absolute Deviation (MAD)12
Skewness0.8296156382
Sum25852
Variance320.7349166
MonotocityNot monotonic
2020-10-30T18:18:53.921824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
30273.6%
 
18273.6%
 
25222.9%
 
16212.8%
 
21212.8%
 
35202.6%
 
40202.6%
 
28202.6%
 
31202.6%
 
19192.5%
 
Other values (72)53871.3%
 
ValueCountFrequency (%) 
220.3%
 
350.7%
 
540.5%
 
620.3%
 
730.4%
 
ValueCountFrequency (%) 
10030.4%
 
8820.3%
 
8710.1%
 
8620.3%
 
8510.1%
 

TempGL
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
3
755 
ValueCountFrequency (%) 
3755100.0%
 
2020-10-30T18:18:54.058887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:18:54.141383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:54.214717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

SpatGL
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2
755 
ValueCountFrequency (%) 
2755100.0%
 
2020-10-30T18:18:54.336681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:18:54.417125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:54.491796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

TempIL
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
3
432 
4
197 
5
125 
2
 
1
ValueCountFrequency (%) 
343257.2%
 
419726.1%
 
512516.6%
 
210.1%
 
2020-10-30T18:18:54.638843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-10-30T18:18:54.736504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:54.868028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

SpatIL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.189403974
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-10-30T18:18:54.976708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.172131767
Coefficient of variation (CV)0.3675080916
Kurtosis-1.152670275
Mean3.189403974
Median Absolute Deviation (MAD)1
Skewness0.09351731699
Sum2408
Variance1.37389288
MonotocityNot monotonic
2020-10-30T18:18:55.091342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
224232.1%
 
419726.1%
 
316021.2%
 
512516.6%
 
1314.1%
 
ValueCountFrequency (%) 
1314.1%
 
224232.1%
 
316021.2%
 
419726.1%
 
512516.6%
 
ValueCountFrequency (%) 
512516.6%
 
419726.1%
 
316021.2%
 
224232.1%
 
1314.1%
 

TLCar
Real number (ℝ≥0)

Distinct505
Distinct (%)66.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1517.87947
Minimum1000
Maximum1999
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-10-30T18:18:55.247735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1069.9
Q11269
median1514
Q31775.5
95-th percentile1953.3
Maximum1999
Range999
Interquartile range (IQR)506.5

Descriptive statistics

Standard deviation287.6553854
Coefficient of variation (CV)0.1895113486
Kurtosis-1.230928041
Mean1517.87947
Median Absolute Deviation (MAD)257
Skewness-0.02810611162
Sum1145999
Variance82745.62073
MonotocityNot monotonic
2020-10-30T18:18:55.429074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
140360.8%
 
160850.7%
 
129350.7%
 
185150.7%
 
195550.7%
 
188150.7%
 
155750.7%
 
168940.5%
 
118340.5%
 
126940.5%
 
Other values (495)70793.6%
 
ValueCountFrequency (%) 
100010.1%
 
100110.1%
 
100310.1%
 
100810.1%
 
101410.1%
 
ValueCountFrequency (%) 
199910.1%
 
199820.3%
 
199610.1%
 
199210.1%
 
199110.1%
 

TLHGV
Real number (ℝ≥0)

Distinct367
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean740.4622517
Minimum501
Maximum999
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-10-30T18:18:55.596830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile527
Q1616
median733
Q3866.5
95-th percentile967.3
Maximum999
Range498
Interquartile range (IQR)250.5

Descriptive statistics

Standard deviation144.0140433
Coefficient of variation (CV)0.1944920797
Kurtosis-1.24071427
Mean740.4622517
Median Absolute Deviation (MAD)123
Skewness0.07525842727
Sum559049
Variance20740.04466
MonotocityNot monotonic
2020-10-30T18:18:55.958597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
92681.1%
 
62670.9%
 
69860.8%
 
84360.8%
 
52260.8%
 
57960.8%
 
70250.7%
 
84050.7%
 
56750.7%
 
79850.7%
 
Other values (357)69692.2%
 
ValueCountFrequency (%) 
50140.5%
 
50230.4%
 
50620.3%
 
50710.1%
 
50810.1%
 
ValueCountFrequency (%) 
99920.3%
 
99830.4%
 
99710.1%
 
99510.1%
 
99410.1%
 

Strasse
Categorical

Distinct14
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
A3
278 
A9
192 
A99
63 
A73
52 
A96
41 
Other values (9)
129 
ValueCountFrequency (%) 
A327836.8%
 
A919225.4%
 
A99638.3%
 
A73526.9%
 
A96415.4%
 
A6374.9%
 
A7354.6%
 
A92212.8%
 
A94141.9%
 
A93121.6%
 
Other values (4)101.3%
 
2020-10-30T18:18:56.110017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)0.4%
2020-10-30T18:18:56.277169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.284768212
Min length2

Kat
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
7
374 
3
323 
2
52 
1
 
6
ValueCountFrequency (%) 
737449.5%
 
332342.8%
 
2526.9%
 
160.8%
 
2020-10-30T18:18:56.441004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:18:56.536722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:56.651024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Typ
Real number (ℝ≥0)

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.288741722
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-10-30T18:18:56.751416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median6
Q36
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.633962452
Coefficient of variation (CV)0.3089510772
Kurtosis1.707856572
Mean5.288741722
Median Absolute Deviation (MAD)0
Skewness-1.778344415
Sum3993
Variance2.669833295
MonotocityNot monotonic
2020-10-30T18:18:56.846053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
657676.3%
 
37610.1%
 
1678.9%
 
7314.1%
 
550.7%
 
ValueCountFrequency (%) 
1678.9%
 
37610.1%
 
550.7%
 
657676.3%
 
7314.1%
 
ValueCountFrequency (%) 
7314.1%
 
657676.3%
 
550.7%
 
37610.1%
 
1678.9%
 

Betei
Real number (ℝ≥0)

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.251655629
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-10-30T18:18:56.953953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7411595962
Coefficient of variation (CV)0.329162056
Kurtosis11.95093789
Mean2.251655629
Median Absolute Deviation (MAD)0
Skewness2.44075112
Sum1700
Variance0.549317547
MonotocityNot monotonic
2020-10-30T18:18:57.053667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
254071.5%
 
312917.1%
 
1435.7%
 
4334.4%
 
560.8%
 
820.3%
 
620.3%
 
ValueCountFrequency (%) 
1435.7%
 
254071.5%
 
312917.1%
 
4334.4%
 
560.8%
 
ValueCountFrequency (%) 
820.3%
 
620.3%
 
560.8%
 
4334.4%
 
312917.1%
 

UArt1
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.866225166
Minimum0
Maximum9
Zeros29
Zeros (%)3.8%
Memory size5.9 KiB
2020-10-30T18:18:57.173944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.87171064
Coefficient of variation (CV)0.6530228895
Kurtosis3.165931553
Mean2.866225166
Median Absolute Deviation (MAD)1
Skewness1.786431065
Sum2164
Variance3.503300718
MonotocityNot monotonic
2020-10-30T18:18:57.281392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
235747.3%
 
320527.2%
 
5638.3%
 
1435.7%
 
0293.8%
 
8283.7%
 
9222.9%
 
770.9%
 
610.1%
 
ValueCountFrequency (%) 
0293.8%
 
1435.7%
 
235747.3%
 
320527.2%
 
5638.3%
 
ValueCountFrequency (%) 
9222.9%
 
8283.7%
 
770.9%
 
610.1%
 
5638.3%
 

UArt2
Real number (ℝ)

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.3059602649
Minimum-1
Maximum9
Zeros4
Zeros (%)0.5%
Memory size5.9 KiB
2020-10-30T18:18:57.385679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile8.3
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.425090766
Coefficient of variation (CV)-7.926162461
Kurtosis9.895298401
Mean-0.3059602649
Median Absolute Deviation (MAD)0
Skewness3.403290888
Sum-231
Variance5.881065224
MonotocityNot monotonic
2020-10-30T18:18:57.483517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
-168991.3%
 
9385.0%
 
8101.3%
 
391.2%
 
240.5%
 
040.5%
 
110.1%
 
ValueCountFrequency (%) 
-168991.3%
 
040.5%
 
110.1%
 
240.5%
 
391.2%
 
ValueCountFrequency (%) 
9385.0%
 
8101.3%
 
391.2%
 
240.5%
 
110.1%
 

AUrs1
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.059602649
Minimum0
Maximum89
Zeros715
Zeros (%)94.7%
Memory size5.9 KiB
2020-10-30T18:18:57.582888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile72
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.23393104
Coefficient of variation (CV)4.24522608
Kurtosis14.57881918
Mean4.059602649
Median Absolute Deviation (MAD)0
Skewness4.04756521
Sum3065
Variance297.0083792
MonotocityNot monotonic
2020-10-30T18:18:57.675621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
071594.7%
 
73131.7%
 
72131.7%
 
8260.8%
 
8950.7%
 
8810.1%
 
8010.1%
 
7510.1%
 
ValueCountFrequency (%) 
071594.7%
 
72131.7%
 
73131.7%
 
7510.1%
 
8010.1%
 
ValueCountFrequency (%) 
8950.7%
 
8810.1%
 
8260.8%
 
8010.1%
 
7510.1%
 

AUrs2
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
753 
81
 
1
80
 
1
ValueCountFrequency (%) 
075399.7%
 
8110.1%
 
8010.1%
 
2020-10-30T18:18:57.799946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.3%
2020-10-30T18:18:57.887872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:57.990210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.002649007
Min length1

AufHi
Real number (ℝ)

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.4953642384
Minimum-1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-10-30T18:18:58.099667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile3
Maximum8
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.381352591
Coefficient of variation (CV)-2.788559374
Kurtosis5.080341107
Mean-0.4953642384
Median Absolute Deviation (MAD)0
Skewness2.510358057
Sum-374
Variance1.90813498
MonotocityNot monotonic
2020-10-30T18:18:58.191787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
-166487.9%
 
37910.5%
 
4101.3%
 
810.1%
 
510.1%
 
ValueCountFrequency (%) 
-166487.9%
 
37910.5%
 
4101.3%
 
510.1%
 
810.1%
 
ValueCountFrequency (%) 
810.1%
 
510.1%
 
4101.3%
 
37910.5%
 
-166487.9%
 

Alkoh
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
-1
744 
1
 
11
ValueCountFrequency (%) 
-174498.5%
 
1111.5%
 
2020-10-30T18:18:58.308208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:18:58.391920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:58.487068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.985430464
Min length1

Char1
Real number (ℝ)

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.6556291391
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-10-30T18:18:58.596526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile4
Maximum6
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.379301122
Coefficient of variation (CV)-2.10378252
Kurtosis13.54443612
Mean-0.6556291391
Median Absolute Deviation (MAD)0
Skewness3.883414444
Sum-495
Variance1.902471586
MonotocityNot monotonic
2020-10-30T18:18:58.695361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
-170993.9%
 
5182.4%
 
4141.9%
 
6101.3%
 
240.5%
 
ValueCountFrequency (%) 
-170993.9%
 
240.5%
 
4141.9%
 
5182.4%
 
6101.3%
 
ValueCountFrequency (%) 
6101.3%
 
5182.4%
 
4141.9%
 
240.5%
 
-170993.9%
 

Char2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
-1
748 
6
 
7
ValueCountFrequency (%) 
-174899.1%
 
670.9%
 
2020-10-30T18:18:58.822595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:18:58.903914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:58.996334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.990728477
Min length1

Bes1
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
-1
581 
6
171 
1
 
3
ValueCountFrequency (%) 
-158177.0%
 
617122.6%
 
130.4%
 
2020-10-30T18:18:59.121970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:18:59.320342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:59.429860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.769536424
Min length1

Bes2
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
-1
755 
ValueCountFrequency (%) 
-1755100.0%
 
2020-10-30T18:18:59.545069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:18:59.618534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:59.691483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Lich1
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
636 
2
84 
1
 
35
ValueCountFrequency (%) 
063684.2%
 
28411.1%
 
1354.6%
 
2020-10-30T18:18:59.812974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:18:59.896256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:59.993578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Lich2
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
-1
636 
4
111 
3
 
8
ValueCountFrequency (%) 
-163684.2%
 
411114.7%
 
381.1%
 
2020-10-30T18:19:00.117373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:19:00.203541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:19:00.302589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.842384106
Min length1

Zust1
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
618 
1
124 
2
 
12
-1
 
1
ValueCountFrequency (%) 
061881.9%
 
112416.4%
 
2121.6%
 
-110.1%
 
2020-10-30T18:19:00.431891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-10-30T18:19:00.519918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:19:00.629571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.001324503
Min length1

Zust2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
-1
748 
2
 
7
ValueCountFrequency (%) 
-174899.1%
 
270.9%
 
2020-10-30T18:19:00.757995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:19:00.846857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:19:00.934902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.990728477
Min length1

Fstf
Categorical

MISSING

Distinct7
Distinct (%)1.0%
Missing39
Missing (%)5.2%
Memory size5.9 KiB
2
360 
1
213 
3
119 
4
 
15
S
 
5
Other values (2)
 
4
ValueCountFrequency (%) 
236047.7%
 
121328.2%
 
311915.8%
 
4152.0%
 
S50.7%
 
520.3%
 
F20.3%
 
(Missing)395.2%
 
2020-10-30T18:19:01.078686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:19:01.180687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:19:01.342501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.103311258
Min length1

WoTag
Categorical

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Fr
150 
Do
131 
Mi
127 
Di
115 
Mo
89 
Other values (3)
143 
ValueCountFrequency (%) 
Fr15019.9%
 
Do13117.4%
 
Mi12716.8%
 
Di11515.2%
 
Mo8911.8%
 
So739.7%
 
Sa658.6%
 
50.7%
 
2020-10-30T18:19:01.486500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:19:01.600742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:19:01.782653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.986754967
Min length0

FeiTag
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
-1
741 
1
 
14
ValueCountFrequency (%) 
-174198.1%
 
1141.9%
 
2020-10-30T18:19:01.932411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:19:02.022981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:19:02.152103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.981456954
Min length1

Month
Categorical

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Jul
114 
Aug
90 
Sep
83 
Oct
65 
Jun
60 
Other values (7)
343 
ValueCountFrequency (%) 
Jul11415.1%
 
Aug9011.9%
 
Sep8311.0%
 
Oct658.6%
 
Jun607.9%
 
Apr577.5%
 
Nov567.4%
 
May537.0%
 
Mar516.8%
 
Dec496.5%
 
Other values (2)7710.2%
 
2020-10-30T18:19:02.298449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T18:19:02.441649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Interactions

2020-10-30T18:17:48.889686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:49.556735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:50.224815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:50.887136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:51.552805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:52.215197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:52.876866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:53.530283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:54.181166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:54.836199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:55.490915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:56.144409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:56.801342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:57.459185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:58.115619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:17:58.778732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:00.723204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:00.750309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:00.964072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:01.160424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:01.351926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:01.552062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:01.741529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:02.920387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:03.104838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:03.290934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:03.485686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:03.688322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:03.862733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:04.039954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:04.216914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:04.393519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:05.853634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:05.880394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:06.063695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:06.242068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:06.413724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:06.600096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:06.772300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:06.962195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:07.135092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:07.310719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:07.489633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:07.672553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:07.841720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:08.010797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:08.181402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:08.348553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:09.367626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:09.395484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:09.569774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:09.739442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:09.895449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:10.067489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:10.229493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:10.411222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:10.575065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:10.737512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:10.903443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:11.074545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-30T18:18:11.388022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-30T18:18:11.699416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:12.551176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-30T18:18:20.445609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-30T18:18:20.845321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:21.037056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:21.232859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:21.433855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:21.618783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-30T18:18:30.900760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:31.046123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-30T18:18:31.906101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:32.719284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:32.752774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:32.925035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-30T18:18:33.380959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-30T18:18:33.695221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-30T18:18:34.306699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:34.453091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:34.598084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:34.744862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:34.889987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:35.616884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:35.638701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:35.949274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:36.090876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:36.219814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:36.361133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:36.494756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:36.640793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:36.773676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:36.910170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:37.048628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:37.191585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:37.322058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:37.451655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:37.579688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:37.709176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:38.442305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:38.464495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:38.608708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:38.747193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:38.878624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:39.015784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:39.158243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:39.307975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:39.441555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:39.573906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:39.709902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:39.848644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:39.999775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:40.128138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:40.256892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:40.389511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:41.111159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:41.134160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:41.276973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:41.415739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:41.709759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:41.883766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:42.022870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:42.170589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:42.304957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:42.444639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:42.580604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:42.717983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:42.845159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:42.970051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:43.101429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:43.228297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:43.941153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:43.962627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:44.101043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:44.235580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:44.362505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:44.506889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:44.636981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:44.780486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:44.910260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:45.040267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:45.172401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:45.307413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:45.434952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:45.561879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:45.688428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:45.815601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:46.535453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:46.557461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:46.714365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:46.863560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:47.006861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:47.166294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:47.462521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:47.623340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:47.768846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:47.911426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:48.057201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:48.205948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:48.346061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:48.487917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:48.628238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:48.767503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-30T18:19:04.007054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-30T18:19:04.649603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-30T18:19:05.275509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-30T18:19:05.348451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-30T18:18:49.948589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:50.845041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T18:18:51.475388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTempGLSpatGLTempILSpatILTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfWoTagFeiTagMonth
0036022265684311006132311714718A32132-100-1-1-1-1-1-10-11-12Di1Jan
1216289139257212005032441293804A336529003-1-1-1-1-10-11-12Mi-1Jan
2416249207015847002832331417502A33622-100-1-1-1-1-1-10-10-1NaNMi-1Jan
35452074833057004232551044780A63632-100-11-1-1-1-10-10-11Mi-1Jan
46285117180675068002832331205643A97133-1720-1-1-1-1-1-10-1121Mi-1Jan
581385564153142004232441803985A97123-100-1-1-1-1-1-1240-11Fr-1Jan
610873225314134387001332551674752A37119-17303-1-1-1-1-10-11-13Sa-1Jan
7112419109029937007832321302634A707727-100-1-1-1-1-1-10-11-12Sa-1Jan
813189762798710911003932441247531A93123-1720-1-15-1-1-10-1121Sa-1Jan
9149920205873917001932331851713A97119-17203-1-1-1-1-10-12-13Sa-1Jan

Last rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTempGLSpatGLTempILSpatILTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfWoTagFeiTagMonth
7451830696199239666775002732551854982A33632-100-1-1-1-16-1241-12Fr-1Dec
7461832729275369118020002132311782567A37623-100-1-1-1-1-1-10-11-12Sa-1Dec
7471833729275369118020002132551782567A33632-100-1-1-1-1-1-10-10-12Sa-1Dec
7481834300160156398285005232441076855A33681-100-1-1-1-1-1-10-10-12Sa-1Dec
7491835300160156398285005232551076855A33622-100-1-16-1-1-10-10-12Sa-1Dec
75018521234685193828004232441928876A93621-100-1-1-1-1-1-10-10-11Do1Dec
7511854477125380415444001432321274619A33622-100-1-1-1-16-1240-12Fr-1Dec
752185823752292296305002132321269784A93632-100-1-1-1-1-1-10-10-1FFr-1Dec
753185956494432447047001632321785638A731429003-1-1-1-1-10-10-12-1Dec
7541864877937883411008832331363741A923622-100-11-1-1-1-1240-12So-1Dec